Novel Ensemble Learning Approach for Predicting COD and TN: Model Development and Implementation
Qiangqiang Cheng,
Ji-Yeon Kim,
Yu Wang,
Xianghao Ren,
Yingjie Guo,
Jeong-Hyun Park,
Sung-Gwan Park,
Sang-Youp Lee,
Guili Zheng,
Yawei Wang,
Young-Jae Lee,
Moon-Hyun Hwang
Affiliations
Qiangqiang Cheng
Key Laboratory of Urban Stormwater System and Water Environment, Ministry of Education, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Ji-Yeon Kim
Institute of Conversions Science, Korea University, 145, Anam-ro, Sungbuk-gu, Seoul 02841, Republic of Korea
Yu Wang
Key Laboratory of Urban Stormwater System and Water Environment, Ministry of Education, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Xianghao Ren
Key Laboratory of Urban Stormwater System and Water Environment, Ministry of Education, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Yingjie Guo
Key Laboratory of Urban Stormwater System and Water Environment, Ministry of Education, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Jeong-Hyun Park
Graduate School of Engineering Practice, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
Sung-Gwan Park
Institute of Conversions Science, Korea University, 145, Anam-ro, Sungbuk-gu, Seoul 02841, Republic of Korea
Sang-Youp Lee
Institute of Conversions Science, Korea University, 145, Anam-ro, Sungbuk-gu, Seoul 02841, Republic of Korea
Guili Zheng
Research Center, Xinhua Pharmaceutical (Shouguang) Co., Ltd., 10 Chayan Road, Shouguang 262700, China
Yawei Wang
Research Center, Xinhua Pharmaceutical (Shouguang) Co., Ltd., 10 Chayan Road, Shouguang 262700, China
Young-Jae Lee
Department of Water Resources, Graduate School of Water Resources, Sungkyunkwan University, Suwon 16419, Republic of Korea
Moon-Hyun Hwang
Institute of Conversions Science, Korea University, 145, Anam-ro, Sungbuk-gu, Seoul 02841, Republic of Korea
Wastewater treatment plants (WWTPs) generate useful data, but effectively utilizing these data remains a challenge. This study developed novel ensemble tree-based models to enhance real-time predictions of chemical oxygen demand (COD) and total nitrogen (TN) concentrations, which are difficult to monitor directly. The effectiveness of these models, particularly the Voting Regressor, was demonstrated by achieving excellent predictive performance even with the small, volatile, and interconnected datasets typical of WWTP scenarios. By utilizing real-time sensor data from the anaerobic–anoxic–oxic (A2O) process, the model successfully predicted COD concentrations with an R2 of 0.7722 and TN concentrations with an R2 of 0.9282. In addition, a novel approach was proposed to assess A2O process performance by analyzing the correlation between the predicted C/N ratio and the removal efficiencies of COD and TN. During a one and a half year monitoring period, the predicted C/N ratio accurately reflected changes in COD and TN removal efficiencies across the different A2O bioreactors. The results provide real-time COD and TN predictions and a method for assessing A2O process performance based on the C/N ratio, which can significantly aid in the operation and maintenance of biological wastewater treatment processes.